Method of predicting sales based on triple-axis mapping of customer value

ABSTRACT

One object of the present invention is to provide a method for determining the current value and future value of customers who purchase specific merchandise, and the resources thereof, in order to provide data by which to select effective sales-promotion investments suitable for such customers, and for predicting sales according to the target and conditions of the investments. A sales-predicting method that classifies customers are into customer-value cells that determine the magnitude of current and future customer value and resources for future customer value, based on purchase data for specific merchandise and using three axes, including a purchase-amount index (first axis), a user-type index (second axis), and a customer-purchase-relevance index (third axis), and that measures changes in the customer-asset cells over time and changes due to sales-promotion investments, and that simulates sales by quantifying causal relationships between sales-promotion investments and sales.

DETAILED DESCRIPTION OF THE INVENTION

1. Field of the Ivention

The present invention relates to a method of predicting sales based ontriple-axis mapping of customer value. With this method, sales of themerchandise of sales of specific manufacturers or specific brands in aspecific market is analyzed by accumulating in a database thepurchase-history data for customers as customers purchase thismerchandise. The method generates a current customer value map based onthe purchase-history data stored in the database. On the map, customersare classified into “cells” according to purchase-amount categoriessorted according to customer purchase sum or quantity of merchandisepurchased and user-type categories sorted according to the number oftypes of merchandise of a specific manufacturer or a specific brandpurchased by a customer in a specified period. The classifications ofuser types estimated from the purchase sum, quantity, and other detailsregarding purchases of all manufacturers' and brands' of merchandise ina given market are quantified based on purchase history, includingquestionnaire data collected at points of sale, membership credit cardsaffiliated with businesses in various industries, or electronictransactions. The customers are sorted into cells according to theabove-described purchase-amount category and user-type category, andthen marketing approaches, such as the sending of direct mail, areimplemented so as to target each cell. Based on customer movement amongcells and increases and decreases of the numbers of customers in cells,it is possible to determine future customer value and future increasesin overall sales for each cell, enabling one to analyze—based only onpurchase data for products of one's own company—whether it will bepossible to expand one's market share for such products.

2. Description of the Related Art

Conventionally it has been impossible to learn the market share of othercompanies' specific products or specific brands in specific markets (forexample, Cosmetic Lotion Y produced by Company X) based solely on one'sown market share. Although there is a need to predict future customervalue so as to analyze the possibility of expanding one's market share,little thought has gone into this conventional problem.

Conventional marketing approaches based solely on data regarding onlyone's own company's sales employ RFM or RFMI (recency, frequency,monetary, item) based on the current value of customers. Theseapproaches assume that customers with good current value will maintainthat value in the future.

However, that is not always the case. To develop an effective marketingapproach, it is necessary to learn not only the current value ofcustomers in terms of purchase sums and quantities, but also theirpotential future value, in order to determine the market share of one'sown company in relation to products of all manufacturers and brands in aspecific market. The future value of customers can be determined throughinterviews with clerks at sales outlets, questionnaires, and the like.It is also necessary to determine potential resources of future value bystudying the various combinations of products purchased by customers.

In order to find an efficient marketing approach (direct mail, etc.),One must analyze whether there has been customer response to previousmarketing approaches.

In view of the foregoing, the present invention has the followingobjectives.

One object of the present invention to provide a method for determiningthe current value and future value of customers who purchase specificmerchandise, and the potential resources thereof, for providing data toselect effective sales-promotion investments suitable for suchcustomers, and for predicting sales according to the target andconditions of the investments.

DISCLOSURE OF THE INVENTION

The above objective and others will be attained by a sales-predictingmethod based on triple-axis mapping of customer value by employing (1) acustomer-value-determination method that uses a customer value-analyzingcomputer system of an institute that collects in a databasepurchase-history data and then analyzes that data, (2) asales-predicting method comprised of the steps of sorting customers intocustomer-value cells that determine the amount of current and futurecustomer value and resources for future customer value, based onspecific-merchandise purchase data collected in the aforementioneddatabase, (3) using a combination of three axes, including apurchase-amount index (first axis), a user-type index (second axis), anda customer-purchase-relevance index (third axis); (4) measuring changesin the customer-asset cells over time and changes due to sales-promotioninvestments; and (5) simulating sales by quantifying causalrelationships between sales-promotion investments and sales.

Further, the purchase-amount index (first axis) serves to classifycustomers in a plurality of classifications in order of purchase sum orpurchase quantity, based on purchase-history data collected for aprescribed period, with said purchase-history data including at least(1) a customer name or customer code, (2) a product code, (3) thequantity of each item purchased, (4) the monetary sum of itemspurchased, and (5) the time of purchase. Said data is collectedregarding transactions of sales outlets, electronic transactionsconducted via the Internet, and direct transactions between amanufacturer and customers, including transactions by telephone and mailwhen customers purchase products of a specific manufacturer or brand ina specific market. The user-type index (second axis) serves to classifycustomers into a plurality of classifications according to user typedetermined by merchandise combinations from customers who purchase themajority of types of merchandise to customers who purchase 0 to 1 typesof merchandise, by combining a plurality of types of merchandise byspecific manufacturers or brands purchased in a specified time period.The customer-purchase-relevance index (third axis) indexes the user typeclassifications extrapolated from purchase sums, quantities, and otherpurchase details for products of all manufacturers and brands, includingthose of other manufacturers and brands, in a specific market, theextrapolated purchase data including product purchase history ofmembership credit cards having a common id and affiliated with aplurality of businesses in various industries, as well as data obtainedthrough questionnaires and/or marketing approaches at sales outlets,questionnaires and/or marketing approaches in electronic transactionsand direct transactions, questionnaires and/or marketing approaches bydirect mail, e-mail, and telephone, and customer data on sales clerks.The sales-predicting method comprises the steps of determining themagnitude of current and future customer value and resources thereof forproducts of specific manufacturers or brands using the triple-axiscombination, providing data for selecting effective investments forsales promotion suited to the customers, and providing a method forpredicting sales based on the target and conditions of the investments.

Further, the customer-purchase relevance is the third index forclassifying customers in a plurality of classifications based onabstracted data according to the monetary sum or quantity of purchasesin a specified period for merchandise of all manufacturers or brands ina specific market, and for combining a plurality of types of merchandiseand classifying customers in a plurality of classifications according tothe merchandise combinations, from customers who purchase the majorityof types of merchandise to customers who purchase 0 to 1 types ofmerchandise.

Further, a sales-predicting method based on triple-axis mapping ofcustomer value employing a customer-value-analyzing computer system ofan institute for collecting and analyzing purchase-history data in adatabase, the purchase-history data including at least (1) a customername or customer code, (2) a product code, (3) the quantity of itemspurchased, (4) the monetary sum of items purchased, and (5) the time ofpurchase, with all said data collected regarding transactions of salesoutlets for the merchandise, electronic transactions conducted via theInternet, and direct transactions, including transactions by telephoneand mail, when customers purchase products of a specific manufacturer orbrand in a specific market. The sales-predicting method comprising thesteps of (1) constructing a customer purchase-history database foraccumulating data on customers that purchase merchandise of specificmanufacturers or brands; (2) dividing customers into m×n cells accordingtwo axes, including a purchase-amount index for classifying customersinto a plurality of categories m in order of purchase sum or quantitybased on the data stored in the customer purchase-history database for aspecified period, and a user-type index for classifying customers byuser type into a plurality of categories n according to combinations ofmerchandise purchased during the same period, with customers beingclassified in a range from customers who purchase a majority of thetypes of merchandise to customers who purchase 0 or 1 kind type of themerchandise; and (3) generating a current value map based on these cellsso as to determine the current customer value in each cell; (4)recording in the customer purchase-history database the user typeclassifications extrapolated from purchase sums, quantities, and otherpurchase details for products of all manufacturers and brands, includingthose of other manufacturers and brands, in a specific market, theextrapolated purchase data including product purchase history ofmembership credit cards having a common id and affiliated with aplurality of businesses in various industries, as well as data obtainedthrough questionnaires and/or marketing approaches at sales outlets,questionnaires and/or marketing approaches in electronic transactionsand direct transactions, questionnaires and/or marketing approaches bydirect mail, e-mail, and telephone, and customer data on sales clerks;(5) determining the magnitude of current and future customer value andresources thereof for products of specific manufacturers or brands basedon data in the database; (6) providing data for selecting effectivesales-promotion investments suited to the customers; and (7) providing amethod for predicting sales suited to the target and conditions of theinvestments.

Further, a sales-predicting method based on triple-axis mapping ofcustomer value employing a customer value-analyzing computer system ofan institute for overseeing purchase-history data that includes at leasteither a customer name or code, product code, quantity, purchase sum,and time of purchase collected through transactions of sales outlets forthe merchandise, electronic transactions conducted via the Internet, anddirect transactions, including transactions by telephone and mail, whencustomers purchase products of a specific manufacturer or brand in aspecific market; and a customer-value determining method capable offinding effective marketing approaches and improving overall sales byselecting not only customers having current value, but also customerswith high future potential value, using a customer-purchase-historydatabase storing the customer-purchase-history data for merchandise ofspecific manufacturers or brands, and a total manufacturer/brandcustomer-purchase database that accumulates data quantifyingcustomer-purchase behavior regarding products of all manufacturers andbrands in a specific market based on responses to questionnaires and/ormarketing approaches at points of sale, in electronic transactions, orin direct transactions, data on sales clerks, and purchase history formembership credit cards with common ids that are affiliated withbusinesses in various industries. The sales-predicting method comprisingthe steps of (1) classifying customers according to a purchase-amountindex, whereby the purchases of merchandise of specific manufacturers orbrands in specific markets are calculated in terms of the monetary sumof purchases, the quantity of items purchased, the volume or the like ofmerchandise purchased by customers or members of household units in aspecified period; (2) classifying customers into a plurality ofcategories m based on this data stored in the customer-purchase-historydatabase, with such categories including at least a heavy-purchaseclassification, a medium-purchase classification, and a light-purchaseclassification; (3) classifying customers according to a user-typeindex, whereby customers who purchase products of specific manufacturersor brands are classified into a plurality of categories n based on datain the purchase-history database, with said categories including anupper category of customers who purchase all types of merchandise of themanufacturers or brands in the specified period, an upper-middlecategory of customers who purchase a majority of types of merchandise, amiddle category of customers who purchase one-half or more of the typesof merchandise, an average category of customers who purchase less thanone-half or an average amount of the merchandise, and a lower categoryof customers who purchase 0 or 1 kind type product; (4) creating acurrent customer-value map by dividing customers into m×n cellsaccording to the customer-amount index and the user-type index andgenerating numerical data indicating the structure and purchasing statusof customer groups for each cell, based on the customer-purchase-historydatabase; (5) classifying customers by relevance, whereby customers areclassified into a plurality of categories m according to purchasemonetary sums or quantities of items purchased within each specifiedperiods for merchandise of all manufacturers or brands based on dataabstracted from the total manufacturer/brand customer-purchase-historydatabase, in order to determine the future value of customers in eachcell; (6) classifying customers by relevance, whereby customers areclassified by user type into at least n categories according tocombinations of merchandise purchased during said specified periods,from customers who purchase a majority of the types of merchandise tocustomers who purchase 0 or 1 kind type of merchandise, based on datastored in the total manufacturer/brand customer-purchase-historydatabase and further dividing the n categories into at least m×n cellsaccording to purchase-amount classifications for each category; (7)classifying customers by relevance by extracting purchase data includingproduct-purchase history of membership credit cards that have a commonid and that are affiliated with a plurality of businesses in variousindustries, as well as data obtained through questionnaires and/ormarketing approaches at sales outlets, questionnaires and/or marketingapproaches in electronic transactions and direct transactions,questionnaires and/or marketing approaches by direct mail, e-mail, andtelephone, and customer data received from sales clerks, and bycategorizing the customers by user type based on data extrapolated frompurchase sums, quantities, and other purchase details for products ofall manufacturers and brands in a specific market; and (8) predictingsales by determining the magnitude of current and future customer valueand resources thereof for products of specific manufacturers or brands;providing effective customers list data for selecting effectivesales-promotion investments suited to the customers; and provides amethod for predicting sales suited to the target and conditions of theinvestments.

Further, customers are arranged in order from largest purchase amount tosmallest purchase amount in the step of classifying customers accordingto purchase amount, and they are placed in one of three categories,including a heavy-purchase classification (h) for customers who accountfor about 50% of the total amount expended on purchases, amedium-purchase classification (m) for customers who account for about30% of the total amount expended on purchases, and a light-purchaseclassification (l) for customers accounting for the remaining 20% of thetotal amount expended on purchases.

Further, numerical data indicating the structure and purchase conditionof customer groups in each cell includes: the customer or householdcomponent percentage; the component percentages of purchase-expenditureamounts, quantities numbers, or volumes; the purchase sum per person;the number of transactions per person; and the number of customers. Inaddition, sales for the near future can be predicted by creating mapsfor consecutive time periods in the current customer-value map-creationstep and comparing data for like cells in each map.

Further, (1) response data for marketing approaches targeting eachcustomer in each cell, including inquiries from customers, requests forinformation materials, store visits, purchases, purchase sums, ongoingpurchases, and ongoing-purchase-amount data, is stored in themarketing-approach-response database, (2) the results of the marketingapproaches for each cell are analyzed based on data in themarketing-approach-response database, and (3) subsequent marketingapproaches are conducted only for cells determined to be highlyeffective.

Further, the specific markets include at least cosmetics, soaps,clothing and apparel, decorations, handbags and travel luggage, homeappliances, computers and peripherals, services including distributionand transport, and alcoholic beverages such as beer, wine, brandy, andwhiskey.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 is an example configuration of a computer system for executing amethod of determining customer value based on three indices according tothe present invention; the figure shows the relationship between thecomputer system, sales terminals, and customer terminals connected via acommunication line.

FIG. 2 shows the flow of operations in the method of determiningcustomer value with three indices according to the present invention.

FIG. 3 shows the flow of operations in the method of determiningcustomer value with three indices according to the present invention.

FIG. 4 shows an embodiment of a current customer-value map, with (a)showing the three-axes construction and (b) showing an example ofspecific data for the hatched portion in (a).

FIG. 5 shows an embodiment of a future customer-value map, with (a)showing the construction and (b) showing an example of specific data forthe hatched portion in (a).

FIG. 6 is a customer-value map showing an embodiment for a specifiedperiod X, with (a) showing the construction and (b) and (c) showingexamples of data.

FIG. 7 is a customer-value map showing an embodiment for a specifiedperiod Y, with (a) showing the construction and (b) and (c) showingexamples of data.

FIG. 8 is a customer-value map showing an embodiment for a specifiedperiod Z, with (a) showing the construction and (b) and (c) showingexamples of data.

FIG. 9 is a customer-value map for an embodiment for a specified periodY, showing the direct effects of the marketing approach, with (a)showing the construction and (b) and (c) showing examples of data.

FIG. 10 is a customer-value map for an embodiment of a specified periodY, showing the indirect effects of the marketing approach, with (a)showing the construction and (b) and (c) showing examples of data.

BEST MODE FOR CARRYING OUT THE INVENTION

FIG. 1 shows an example of a configuration of a computer system 1designed to determine customer value based on three indices according tothe present invention. FIG. 1 also shows the relationship between thecomputer system 1, sales terminals 3 at points of sale, and customerterminals 4. The sales terminals 3 and customer terminals 4 areconnected to the computer system 1 via a communications circuit 2. Thenumeral 9 indicates a purchase form used when making direct transactionswith a customer.

The part labeled 10 is a processing server for analyzing customer value.The processing server 10 includes analyzing programs 10 a that determinecustomer value according to the present invention.

The numeral 11 indicates a database server for managing a database 12.The database 12 includes at least the following sub-databases: acustomer-purchase-history database 12 a, a total manufacturer/brandcustomer-purchase-history database 12 b, and amarketing-approach-response database 12 c.

The numeral 13 is a Web server 13. The numeral 14 is a terminal 14allowing the input of form data 9 by manufacture when conducting directtransactions with a customer. The terminal 14 also serves as adocument-outputting terminal used in marketing approaches, such as thesending of direct mail or questionnaires to customers. The numeral 15refers to a local area network (LAN).

The customer-purchase-history database 12 a stores purchase-history datathat is acquired when a customer purchases merchandise of specificmanufacturers or brands in a specific market. This purchase-history dataincludes at least customer codes, products codes, quantities purchased,monetary amounts of purchase, and the time of purchases—data acquired intransactions at sales outlets, in electronic transactions with customersvia the Internet, or in direct transactions with customers.

A current customer-value map is created from the purchase-history datastored in the customer-purchase-history database 12 a, as describedbelow. A map is created by dividing the customers among m×n cellsaccording to two axes. The first axis is a purchase-amount index forclassifying customers in a plurality of categories m according toamounts of purchases in each specified period. The customers arearranged in order according to the monetary sums of purchases or thequantities of items purchased, based on the purchase-history data. Thesecond axis is a user-type index for classifying customers in aplurality of user-type categories according to combinations ofmerchandise purchased by customers during said specified period—fromcustomers who purchase a majority of the plurality of the varieties ofmerchandise to customers who purchase 0 or 1 kind type of merchandise.In this way, it is possible to analyze the current value of customers ineach cell of the map.

By analyzing maps for current customer value in two consecutive periods,it is possible to predict percentages of customer movements and theinflow/outflow of customers for each cell in the next specified period.

Here, a majority of the purchase-history data for transactions at salesoutlets is transmitted from the sales terminals 3 shown in FIG. 1 to thecomputer system 1 via the communications circuit 2.

Purchase-history data of electronic purchases by customers istransmitted to the computer system 1 via the communications circuit 2from the customer terminals 4, shown in FIG. 1.

Purchase-history data for direct transactions with customers is inputvia the terminal 14 using a purchase record.

The total manufacturer/brand customer-purchase-history database 12 bstores extracted data for quantifying the purchase behavior ofcustomers; in other words, each manufacturer's or each brand'spercentage of sales of the total sales of all merchandise of allmanufacturers and brand in a specific market, including merchandise ofother manufacturers and other brands. Data stored in the databaseincludes—in addition to the responses to questionnaires and/or marketingapproaches at sales outlets—questionnaires and/or marketing approachesin electronic transactions and direct transactions (includingtransactions by telephone and mail), customer behavior as reported bysales clerks, responses to questionnaires and/or marketing approachesvia direct mail or e-mail, extracted data relating to purchases,including the product-purchase history of membership credit cards with acommon id that are affiliated with various businesses in a plurality ofindustries.

A map of future customer value according to the aforementioned threeindices is created so as o quantify customer-purchase behavior based ondata extracted from the total manufacturer/brandcustomer-purchase-history database 12 b, as described below.

The method for creating the map comprises relevance classifications (a),(b), and (c). The relevance classification (a) classifies customers bythe monetary amounts of purchases, placed in order according to thepurchase's monetary sum or the like and based on data extracted for eachspecified period. The relevance classification (b) classifies customersby user type, based on data stored in the database 12 b, into at least ncategories according to combinations of merchandise purchased duringsaid specified period, from customers who purchase a majority of thetypes of merchandise to customers who purchase 0 or 1 kind type ofmerchandise. The n categories are further divided into at least m×ncells according to purchase-amount classifications for each category.The relevance classification (c) classifies customers according tovarious marketing approaches and/or sales correspondence, includingdirect mail or e-mail sent to customers of each cell.

Here, sales correspondence is distributed a plurality of times over aninterval equal to or greater than the specified period mentioned above.An “approach number” is attached to each distributed correspondence andis accumulated in the total manufacturer/brand customer-purchase-historydatabase 12 b. The terminal 14 shown in FIG. 1 serves to distribute mostsales-correspondence documents. However, these documents can also bedistributed via the sales outlets.

The marketing-approach-response database 12 c stores responses to themarketing approaches. Included in these recorded responses areinformation obtained from inquiries from customers, requests forinformation materials (catalogs), store visits, purchases, monetary sumsof purchases, ongoing purchases or repeat purchases, and cumulative dataregarding ongoing purchases. The input route for this data is the sameas that for the customer-purchase-history database 12 a.

Next, the process of operations performed by the analyzing programs 10 astoring the method of determining customer value according to thepresent invention will be described with reference to the flowcharts inFIGS. 2 and 3.

Purchase-history data is collected via routes (a), (b), and (c),described below, each time a customer purchases merchandise of specificmanufacturers or brands in a specific market.

Route (a) is a transmission path from a sales terminal 3 to the computersystem 1 via the communications circuit 2.

Route (b) is a transmission path from a Web-page input screen on acustomer terminal 4 to the computer system 1 via the communicationscircuit 2.

Route (c) is a path for inputting data via the terminal 14 of thecomputer system 1 using data on a form created through directtransactions with a customer (S 21).

Such data is accumulated one by one in the customer-purchase-historydatabase 12 a of the computer system 1 (S 22).

Using the analyzing programs 10 a, the processing server 10 of thecomputer system 1 begins to generate a current customer-value map byclassifying customers in the order described below for each specifiedperiod (a period of about 3 months or 6 months; S 23).

Next, will be described the process for generating a purchase-amountindex.

The amount of money spent by customers or members of household units formerchandise of specific manufacturers or brands in specific markets isdivided into a plurality of classifications m based on data in thecustomer-purchase-history database 12 a. The plurality ofclassifications m includes at least a heavy-purchase classification (H),a medium-purchase classification (M), and a light-purchaseclassification (L) determined according to the amounts of purchases foreach specified period t. The amount of a purchase is the monetary sum ofthe purchase, the quantity number, or the volume of the products.

Next, an example of the above purchase-amount classification ispresented. First, customers are listed in order of purchase amounts,from greatest to least, in a specified period t. Customers accountingfor about 50% of the overall purchase amount are classified in the heavyclassification (H). Those occupying about 30% of the total purchaseamount are classified in the medium classifications (M), and theremaining customers are allocated to the light classification (L) (S24).

Next, the process for generating the user-type index will be described.

In this example, customers who purchase merchandise of specificmanufacturers or brands have purchased basic types of merchandise A, B,or C, based on the data in the customer-purchase-history database 12 a.(for example, when the specific market is cosmetics, the types ofproducts might include a basic cosmetic A, a special whitening cosmeticB for use as a foundation, and a special age-related cosmetic Ceffective for wrinkles and the like.) This data is classified in sixcategories, including customer category (ABC) for customers who purchaseall three types, customer category (AB) for customers who purchase typesA and B, customer category (AC) for customers who purchase types A andC, customer category (A) for customers who purchase only type A,customer category (B) for customers who purchase only type B, andcustomer category (C) for customers who purchase only type C (S 25).

Next, the customers are divided into m×n cells (3×6 cells in thisexample) according to the customer-amount index and the user-type index(S 31).

Next, a current customer-value map is generated based on thecustomer-purchase-history database 12 a by calculating numerical dataindicating the structure and purchasing status of customer groups foreach cell (S 32).

Here, the numerical data indicating the structure and purchasing statusof customer groups in each cell includes the customers in each cell as apercentage of the total, their purchase sums as percentages of the totalpurchases, the purchase sum per person, the number of store visits perperson, the number customers, and the like.

FIG. 4 shows the example described above. FIG. 4( a) shows thetriple-axis configuration, while (b) shows a specific example of datafor the diagonal-hatching portion of FIG. 4( a). In FIG. 4(b), thehorizontal axis indicates the user-type index, divided into the sixcategories (ABC), (AB), (AC), (A), (B), and (C). The vertical axisindicates the purchase-amount index divided into the three categories(H), (M), and (L). Hence, the customers are ultimately divided among3×6=18 cells. Each cell includes four types of data: the customerpercentage, the percentage of total purchases, the amount expended onpurchases, and the number of store visits per person. FIG. 4( b) is oneexample of specific data.

At the same time that a current customer-value map is generated, asdescribed above, for the current point in time, the marketing approachdescribed below is conducted so as to predict future customer value.

This marketing approach comprises a relevance classification (α) inwhich customers are classified according to the monetary amounts oftheir purchase within a specified period into one of three categories(H), (M), or (L), based on data abstracted from the totalmanufacturer/brand customer-purchase-history database 12 b; a user-typerelevance classification (β) whereby customers are classified by usertype into one of seven categories, including the six categories (ABC),(AB), (AC), (A), (B), and (C) and an additional category (D) forcustomers who also purchase products of other manufacturers and brands;and a sales-correspondence approach (γ) in which the terminal 14 outputssales correspondence such as questionnaires via direct mail, e-mail, andtelephone to customers of each cell (S 33).

After-sales correspondence is distributed to the customers, and customerdata is recorded in the total manufacturer/brandcustomer-purchase-history database 12 b for a fixed period Z (forexample, about 2-3 months) following the beginning of the marketingapproach (S 34).

As described above, sales correspondence is distributed to customers ofeach cell. Subsequently, the responses are quantified to generate acustomer-purchase-relevance index.

A map of customer value is generated for this fixed period Z based onthe data stored in the total manufacturer/brandcustomer-purchase-history database 12 b. Future data is predicted bycomparing the current customer-value map to data before the marketingapproach and creating a future customer-value map by calculating thepercentage of repeat customers, ex-customers, and new customers for eachcell and the percentage of increase or decrease in the total number ofcustomers and total sales for the specific merchandise (S 35).

At the same time that a current customer-value map is generated for thecurrent point in time, as described above, a future customer-value mapis begun to be generated by classifying customers in the order describedbelow in order to predict customer value for the future.

Now the process for generating a customer-purchase-relevance index willbe describes.

First, customers are classified into a total of 21 cells according tothe relevance classification (α), in which customers are classified inthree categories (H), (M), and (L) according to their purchase amountsbased on data extracted from the total manufacturer/brandcustomer-purchase-history database 12 b for a specified time period; andthe user-type relevance classification (β), whereby customers areclassified into seven categories, including the six user type (ABC),(AB), (AC), (A), (B), and (C) and an additional customer bracket (AD)for customers purchasing a combination of products including types notsold by the specific manufacturer or brand.

Next, the customers are divided into m×n cells (21×6 cells in thepresent embodiment) based on the customer-purchase-relevance index anduser-type index.

The total manufacturer/brand customer-purchase-history database 12 b isprepared in advance as described below.

That is, responses to questionnaires filled out at sales offices,responses to questionnaires in electronic transactions and directtransactions (including transactions by telephone and mail), dataregarding customer behavior from clerks at points of sale, and responsesto questionnaires by direct mail, e-mail, and telephone are recorded inthe total manufacturer/brand customer-purchase-history database 12 balong with extracted data relating to purchases, including theproduct-purchase history of membership credit cards having a common idthat are affiliated with a plurality of businesses in variousindustries.

Next, a future customer-value map is generated based on the totalmanufacturer/brand customer-purchase-history database 12 b andcustomer-purchase-history database 12 a by calculating numerical dataindicating the structure and purchase status of the customer group ineach cell within a fixed period X (for example, about 2-3 months).

Here, the numerical data indicating the structure and purchase statusfor each cell includes the customers in each cell as a percentage of thetotal, their purchase sums as a percentage of the total, the purchasesum per person, the number of transactions per person, the number ofcustomers, and the like.

FIG. 5 shows the example described above. FIG. 5( a) shows thetriple-axis configuration, while FIG. 5( b) shows a specific example ofdata for the portion indicated by diagonal hatching. Here, thehorizontal axis represents the user-type index for a specificmanufacturer/brand in a specific industrial market and includes the sixcategories (ABC), (AB), (AC), (A), (B), and (C). The vertical axisrepresents the customer-purchase-relevance index and is divided into 21categories, including the relevance classifications (H), (M), and (L)determined by purchase amounts x, and the user-type relevanceclassifications (ABC), (AB), (AC), (A), (B), (C), and (AD). Hence, thecustomers are ultimately divided into 126 cells (6×21), whereby eachcell includes the customer percentage of the total, the purchase sumpercentage of the total, the purchase sum, and number of store visitsper person.

In the customer-value determining method described above, the futurecustomer-value map allows one to determine what percentage of one's ownmerchandise is being purchased in relation to the purchases ofmerchandise of all manufacturers and brands in a specific market, orwhat the purchasing behavior of customers is towards new product fields.In this way, the customer-value determining method enables efficientlyapproaching not only customers that currently have good value forspecific manufacturers or brands, but also customers with high potentialfor the future.

In addition, by comparing the future customer-value map to the currentcustomer-value map, it is possible to quantify the magnitude ofpotential value of customers, thereby further improving the efficiencyof the marketing approach.

Take, for example, customers placed in the relevance category (H)according to purchase amounts in the future customer-value map and inthe purchase-amount category (L) in the current customer-value map. Thefuture value of these customers can be estimated by the difference(H)-(L). By using the future value map in this way, it is possible todetermine resources of future value of customers.

Response data for approaches executed using the method for determiningcustomer value with three indexes configured by future and currentcustomer-value maps is accumulated in the marketing-approach-responsedatabase 12 c for storing such responses. These responses includeinquiries from customers, requests for information materials (catalogs),store visits, purchases, purchase sums, ongoing purchases or repeatpurchases, and data regarding ongoing-purchase amounts. The input routefor data concerning specific manufacturers and brands is the same asthat for the customer-purchase-history database 12 a, while that fordata concerning all manufacturers and brands is the same as that for thetotal manufacturer/brand customer-purchase-history database 12 b.

By conducting marketing approaches for customers in each cell andquantifying the responses to these approaches as described above, it ispossible to determine customer value with consistently high precision byupdating the purchase-amount index and user type for specificmanufacturers/brands and the customer-purchase-relevance index for allmanufacturers/brands at regular intervals.

Further, by continuously creating current customer-value maps for eachspecified period and comparing data for each cell, it is possible topredict customer movement in the near future and the total sales thatwill be generated by those customers.

In other words, according to the procedure described above, a currentcustomer-value map and future customer-value map are generated for eachspecified period X, based on the customer-purchase-history database 12 aand the total manufacturer/brand customer-purchase-history database 12b, and marketing approaches are conducted for customers in each cell. Acurrent customer-value map and future customer-value map are generatedfor each specified period Y after marketing approaches are conducted andinclude response data to these approaches stored in themarketing-approach-response database 12 c. Assuming that the samemarketing approach has been executed for customers in the specifiedinterval Y as that conducted in the specified interval X, it is possibleto estimate a current customer-value map for a specified interval Zafter the marketing approach using the same procedure.

The example described above is shown in FIGS. 6, 7, and 8. FIG. 6 showsthe example for the specified period X, FIG. 7 for the specified periodY, and FIG. 8 for the specified period Z. Within each of these examples,(b) is the current customer-value map, while (c) is the futurecustomer-value map. Marketing approaches were conducted only forcustomers in the purchase-amount category (H) x the user-type category(ABC) cell for the current customer-value map during the x period and yperiod.

Now a more-detailed description of the drawings will be presented. Theeffects of the marketing approaches for each cell were estimated in themanner described below by comparing the current customer-value map forthe specified period X prior to conducting the marketing approach(indicated in FIG. 6( b)) and the future customer-value map (indicatedin FIG. 6( c)), and between the current customer-value map for thespecified period Y after beginning the marketing approach (indicated inFIG. 7( b)) and the future customer-value map (indicated in FIG. 7( c)).FIG. 8 shows examples of the current customer-value map and futurecustomer-value map for the specified period Z after the marketingapproach was begun.

Take for assumptive example, a cell SMN in column m and row n in thecurrent customer-value map configured of the purchase-amount index andthe user-type index. The cell SMN includes the repeat-customerpercentage T % of customers in the cell SMN that have remained in thecell SMN due to the influence of the marketing approach; and theex-customers percentage who have migrated out of the cell SMN (detailsregarding the percentage of ex-customers include both customers thathave migrated to cells outside the SMN cell and customers who havestopped purchasing altogether). This completes the description ofcustomers directly affected by the marketing approach.

FIG. 9 shows the example described above. FIG. 9 quantifies the directeffect of marketing approaches for the specified period following theapproach. During the specified period after conducting a marketingapproach aimed at customers in the purchase-amount category (h) x theuser-type category (ABC) cell in the current customer-value map, 44.8%of the customers remained in the same cell, 37.1% migrated to othercells, and 18.1% stopped purchasing altogether.

In terms of indirect effects on the cell SMN, there is also anew-customer percentage consisting of customers who have migrated fromother cells to the cell SMN (the details of the new-customer percentageinclude both customers who have come to the SMN cell from other cellsand new customers who had not purchased in the previous period).

FIGS. 10( a), (b), and (c) illustrate the above example. FIG. 10( b)quantifies the indirect effects of a marketing approach during aspecified period after conducting the marketing approach. During thisspecified period after conducting a marketing approach targetingcustomers in the purchase-amount category (H) ×user-type category (ABC)cell in the current value map X, 13.3% of customers in the (M)×(AB) cellnot targeted by the approach remained in the same cell, 3.8% migrated tothe (H)×(ABC) cell, 42.5% migrated to other cells, and 40.4% stoppedpurchasing altogether. FIG. 10( c) shows that 21,432 previousnonpurchasers began purchasing. Of these new customers, 5.7% migrated tothe (H)×(ABC) cell, while 94.3% migrated to other cells.

It is possible to find the purchase sums, absolute-count values, andpercentages of increase and decrease for the above repeat customers, newcustomers, and ex-customers.

With this data, it is possible to perform more-accurate predictions offuture values.

INDUSTRIAL APPLICABILITY

The sales-predicting method based on triple-axis mapping of customervalue of the present invention has the following effects. Thesales-predicting method of the present invention can determine themagnitude and resources of current value and future value for customersof specific merchandise; can provide data for selecting effectivesales-promotion investments suited to these customers; and can provide asales-predicting method according to targets and conditions of theinvestments.

1. A sales-predicting method which is performed by execution of computerreadable program code using at least one processor of at least onecomputer system, based on triple-axis mapping of customer value,employing a computer system that collects and analyzes purchase-historydata in a database; the computer system comprising a processing serverhaving a group of analyzing programs for analyzing customer value, adatabase server for managing various databases, a web server, and a datainput/output terminal connected to a communication line; the databaseserver comprising a customer-purchase-history database for accumulatingpurchase-history data including (1) a customer name or code, (2) aproduct code, (3) the quantity of items purchased, (4) the amountexpended on purchases, and (5) the time of purchase, with thepurchase-history data collected through transactions of sales outletsfor the merchandise, electronic transactions conducted via the Internet,and direct transactions including transactions by telephone and mailwhen customers purchase products of a specific manufacturer or brand ina specific market; and a total manufacturer/brandcustomer-purchase-history database for accumulating purchase-historydata including product-purchase history of membership credit cardshaving a common ID and affiliated with a plurality of businesses invarious industries, as well as data obtained through questionnairesand/or marketing approaches at sales outlets, questionnaires and/ormarketing approaches in electronic transactions via the web server,questionnaires and/or marketing approaches by direct mail, email, andtelephone, and customer data reported by sales clerks; thesales-predicting method comprising the steps: creating, using at leastone of the processors, a purchase-amount index (first axis) of cells bysearching the customer-purchase-history database at specified periodsand classifying customers into a plurality of classifications in orderof the amounts expended on purchases or the quantity of items purchased,based on purchase-history data extracted during the search; creating,using at least one of the processors, a user-type index (second axis) ofcells by searching the customer-purchase-history database at saidspecified periods and classifying customers into a plurality ofclassifications according to user type as determined by combinations ofmerchandise from customers who purchase the majority of types of themerchandise to customers who purchase 0 or 1 type of the merchandise, bycombining a plurality of types of merchandise by specific manufacturersor brands purchased in each specified period; creating, using at leastone of the processers, a current customer-value map by dividingcustomers into cells according to a product of the plurality ofclassifications in said first index and said second index for analyzingthe current value of customers in each cell; classifying, using at leastone of the processers, customers by relevance by searching said totalmanufacturer/brand customer-purchase-history database at said specifiedperiods and classifying customers by purchase amount into a plurality ofcategories according to purchase monetary sums or quantities of itemspurchased based on data abstracted from the total manufacturer/brandcustomer-purchase-history database; classifying, using at least one ofthe processers, customers by relevance by searching said totalmanufacturer/brand customer-purchase-history database at said specifiedperiods and classifying customers by user type into a plurality ofcategories according to combinations of merchandise purchased duringsaid specified periods, from customers who purchase the majority of thetypes of merchandise to customers who purchase 0 or 1 type of themerchandise, based on data extracted during the search; creating, usingat least one of the processers, a customer-purchase-relevance index(third axis) based on a product of the plurality of classifications fromeach of said two relevance classifying steps; and creating, using atleast one of the processers, a future customer-value map by dividingcustomers into cells formed by a product of each plurality ofclassifications in said user-type index (second axis) and saidcustomer-purchase-relevance index (third axis) to analyze the futurevalue for customers in each cell.
 2. The sales-predicting method basedon triple-axis mapping of customer value as recited in claim 1, wherein:the database further comprises a marketing-approach response databasefor accumulating responses to said marketing approaches; and furthercomprising the steps of: performing, using at least one of theprocessers, various marketing approaches and/or sales correspondenceincluding direct mail or e-mail sent to customers of each cell aplurality of times over an interval equal to or greater than saidspecified period, in addition to said two relevance classifying stepsfor creating said customer-purchase-relevance index (third axis);accumulating, using at least one of the processers, responses to salescorrespondence in said marketing-approach response database andgenerating the customer-purchase-relevance index (third axis) forsorting customers according to cell by quantifying the responses; andcreating, using at least one of the processers, a future customer-valuemap according to cells in which customers are sorted using the threeaxes for analyzing the future value of customers in each cell.
 3. Asales-predicting method which is performed by execution of computerreadable program code using at least one processor of at least onecomputer system, based on triple-axis mapping of customer valueemploying a customer value-analyzing computer system for collecting andanalyzing purchase-history data in a database, with the purchase-historydata including at least (1) a customer name or customer code, (2) aproduct code, (3) the quantity of items purchased, (4) the amountexpended on a purchase, and (5) the time of purchase, with said datacollected through transactions of sales outlets for the merchandise,electronic transactions conducted via the Internet, and directtransactions including transactions by telephone and mail when customerspurchase products of a specific manufacturer or brand in a specificmarket; the computer system comprising a processing server having agroup of analyzing programs for analyzing customer value, a databaseserver for managing various databases, a web server, and a datainput/output terminal connected to a communication line; the databaseserver comprising a customer-purchase-history database for accumulatingthe purchase-history data concerning customers that purchase merchandiseof a specific manufacturer or brand; and a total manufacturer/brandcustomer-purchase-history database for accumulatingcustomer-purchase-history data extrapolated from amounts expended onpurchases, quantities of items purchased, and other purchase details forproducts of all manufacturers and brands, including those of othermanufacturers and brands, in a specific market, with the extrapolatedpurchase-history data including product-purchase history of membershipcredit cards having a common ID and affiliated with a plurality ofbusinesses in various industries, as well as data obtained throughquestionnaires and/or marketing approaches at sales outlets,questionnaires and/or marketing approaches in electronic transactionsvia the web server and direct transactions via the data input/outputterminal, and questionnaires and/or marketing approaches by direct mail,e-mail, and telephone; the sales-predicting method comprising the stepsof: searching, using at least one of the processers, saidcustomer-purchase-history database, dividing customers into m×n cellsaccording to two axes, including a purchase-amount index for classifyingcustomers into a plurality of categories m in order of purchase sum orquantity based on data stored in the customer purchase-history databasefor each specified period, and a user-type index for classifyingcustomers by user type into a plurality of categories n according tocombinations of merchandise purchased during each specified period, withcustomers being classified in a range from customers who purchase amajority of the types of merchandise to customers who purchase 0 or 1type of the merchandise, and generating a current customer-value mapbased on these cells so as to determine the current customer value ineach cell; and searching, using at least one of the processors, thetotal manufacturer/brand customer-purchase-history database, dividingcustomers into cells according to a customer-purchase-relevance index(third axis) for cells classified according to a product of categoriesfor customer-purchase-amount relevance and said user-type relevance inthe specified periods, and creating a future customer-value map based onsaid third axis to determine the future value of customers in each cell;determining, using at least one of the processers, the magnitude ofcurrent and future customer value and resources thereof for products ofspecific manufacturers or brands based on data in thecustomer-purchase-history database and total manufacturer/brandcustomer-purchase-history database, providing data for selectingeffective sales-promotion investments suited to the customers, andproviding a method for predicting sales suited to the target andconditions of the investments.
 4. A sales-predicting method based ontriple-axis mapping of customer value capable of finding effectivemarketing approaches and improving overall sales by selecting not onlycustomers having current value, but also customers having highfuture-value potential, using a customer value-analyzing computer systemof an institute for overseeing purchase-history data, with said dataincluding at least (1) a customer name or customer code, (2) a productcode, (3) the quantity of items purchased, (4) the amount expended onpurchases, and (5) the time of purchases, with all said data collectedthrough transactions of sales outlets for the merchandise, electronictransactions conducted via the Internet, or direct transactionsincluding transactions by telephone and mail when customers purchaseproducts of a specific manufacturer or brand in a specific market; thecomputer system comprising at least a processing server having a groupof analyzing programs for analyzing customer value, acustomer-purchase-history database for accumulating purchase-historydata on products of specific manufacturers or brands required for theanalyzing programs, and a total manufacturer/brandcustomer-purchase-history database for accumulating data for quantifyingthe purchase behavior of customers for merchandise of all manufacturersor brands in a specific market based on questionnaires and/or marketingapproaches at sales outlets, in electronic transactions, and in directtransactions, customer data reported by sales clerks, andproduct-purchase history of membership credit cards with a common IDthat are affiliated with businesses in various industries; thesales-predicting method comprising the steps of: classifying, using atleast one of the processors, customers according to a purchase-amountindex (first axis), whereby the amount of money expended on merchandiseof specific manufacturers or brands in specific markets is determined bythe amount expended on purchases, the quantity of items purchased, thevolume of items purchased, or the like of merchandise purchased bycustomers or members of household units in each specified period, andwhereby customers are classified into a plurality of categories m basedon this data, which is stored in the customer-purchase-history database,and with the categories including at least a heavy-purchaseclassification, a medium-purchase classification, and a light-purchaseclassification; classifying, using at least one of the processers,customers according to a user-type index (second axis), wherebycustomers who purchase products of specific manufacturers or brands areclassified into a plurality of categories n based on data in thepurchase-history database, with the categories including an uppercategory for customers who purchase all types of merchandise of themanufacturers or brands in the specified period, an upper-middlecategory for customers who purchase a majority of the types ofmerchandise, a middle category for customers who purchase one-half ormore of the types of merchandise, an average category for customers whopurchase a one-half or an average amount of the merchandise, and a lowercategory for customers who purchase 0 or 1 type of products; creating,using at least one of the processers, a current customer-value map bydividing the customers into m×n cells according to the customer-amountindex and the user-type index and generating numerical data indicatingthe structure and purchasing status of customer groups for each cell,based on the customer-purchase-history database; classifying, using atleast one of the processers, customers by relevance, whereby customersare classified into a plurality of categories m according to purchaseamounts ordered by amounts expended or quantities of purchases within aspecified period for merchandise of all manufacturers or brands, basedon data abstracted from the total manufacturer/brandcustomer-purchase-history database, in order to determine the futurevalue of customers in each cell; classifying, using at least one of theprocessers, customers by relevance, whereby customers are classified byuser type into at least n categories according to combinations ofmerchandise purchased during the same period, from customers whopurchase a majority of the types of the merchandise to customers whopurchase 0 or 1 type of the merchandise, based on data stored in thetotal manufacturer/brand customer-purchase-history database, and furtherdividing the n categories into at lest m×n cells according topurchase-amount classifications for each category; creating, using atleast one of the processers, a customer-purchase-relevance index (thirdaxis) by conducting such marketing approaches and/or salescorrespondence as questionnaires and/or marketing approaches at salesoutlets, questionnaires and/or marketing approaches in electronictransactions and direct transactions, questionnaires and/or marketingapproaches by direct mail, e-mail, and telephone, and customer datareported by sales clerks, quantifying responses to these marketingapproaches, and updating the content of the cells for each specifiedperiod; creating, using at least one of the processers, a futurecustomer-value map by dividing customers into cells formed by a productof each plurality of categories in said third axis and said second axis;and determining, using at least one of the processers, a relationshipbetween the magnitude of current and future customer value and resourcesthereof for specific products and the effects of various marketingapproaches, providing data for selecting effective sales-promotioninvestments suited to the customers, and providing a method forpredicting sales suited to the target and conditions of the investments.5. The sales-predicting method based on triple-axis mapping of customervalue as recited in claim 4, whereby customers are arranged in orderfrom largest purchase amount to smallest in the step for classifyingcustomers according to purchase amount, and whereby customers are placedin one of three categories, including a heavy-purchase classification(H) for customers who account for about 50% of the total amount expendedon purchases, a medium-purchase classification (M) for customers whoaccount for about 30% of the total, and a light-purchase classification(L) for customers accounting for the remaining 20%.
 6. Thesales-predicting method based on triple-axis mapping of customer valueas recited in claim 4, whereby numerical data indicating the structureand purchase condition of customer groups in each cell includes thecustomer or household component percentage, the component percentage ofpurchase sums, quantities, or volume, the amount expended per person,the number of transactions per person, and the number of customers, andwhereby sales for the near future can be predicted by creating maps forconsecutive time periods in the current customer-value map-creation stepand comparing data for like cells in each map.
 7. The sales-predictingmethod based on triple-axis mapping of customer value as recited inclaim 4, wherein response data for marketing approaches targets eachcustomer in each cell, with said response data including inquiries fromcustomers, requests for information materials, store visits, purchases,purchase sums, ongoing purchases, and data regarding ongoing-purchaseamounts, with said data stored in the marketing-approach-responsedatabase, and with the results of the marketing approaches for each cellanalyzed based on data in the marketing-approach-response database, andsubsequent marketing approaches conducted only for cells determined tobe highly effective.
 8. The sales-predicting method based on triple-axismapping of customer value as recited in claim 1, whereby the specificmarkets include at least cosmetics, soaps, clothing and apparel,decorations, handbags and travel luggage, home appliances, computers andperipherals, services including distribution and transport, andalcoholic beverages including beer, wine, brandy, and whiskey.
 9. Thesales-predicting method based on triple-axis mapping of customer valueas recited in claim 5, whereby numerical data indicating the structureand purchase condition of customer groups in each cell includes thecustomer or household component percentage, the component percentage ofpurchase sums, quantities, or volume, the amount expended per person,the number of transactions per person, and the number of customers, andwhereby sales for the near future can be predicted by creating maps forconsecutive time periods in the current customer-value map-creation stepand comparing data for like cells in each map.
 10. The sales-predictingmethod based on triple-axis mapping of customer value as recited inclaim 5, wherein response data for marketing approaches targets eachcustomer in each cell, with said response data including inquiries fromcustomers, requests for information materials, store visits, purchases,purchase sums, ongoing purchases, and data regarding ongoing-purchaseamounts, with said data stored in the marketing-approach-responsedatabase, and with the results of the marketing approaches for each cellanalyzed based on data in the marketing-approach-response database, andsubsequent marketing approaches conducted only for cells determined tobe highly effective.
 11. The sales-predicting method based ontriple-axis mapping of customer value as recited in claim 6, whereinresponse data for marketing approaches targets each customer in eachcell, with said response data including inquiries from customers,requests for information materials, store visits, purchases, purchasesums, ongoing purchases, and data regarding ongoing-purchase amounts,with said data stored in the marketing-approach-response database, andwith the results of the marketing approaches for each cell analyzedbased on data in the marketing-approach-response database, andsubsequent marketing approaches conducted only for cells determined tobe highly effective.
 12. The sales-predicting method based ontriple-axis mapping of customer value as recited in claim 2, whereby thespecific markets include at least cosmetics, soaps, clothing andapparel, decorations, handbags and travel luggage, home appliances,computers and peripherals, services including distribution andtransport, and alcoholic beverages including beer, wine, brandy, andwhiskey.
 13. The sales-predicting method based on triple-axis mapping ofcustomer value as recited in claim 3, whereby the specific marketsinclude at least cosmetics, soaps, clothing and apparel, decorations,handbags and travel luggage, home appliances, computers and peripherals,services including distribution and transport, and alcoholic beveragesincluding beer, wine, brandy, and whiskey.
 14. The sales-predictingmethod based on triple-axis mapping of customer value as recited inclaim 4, whereby the specific markets include at least cosmetics, soaps,clothing and apparel, decorations, handbags and travel luggage, homeappliances, computers and peripherals, services including distributionand transport, and alcoholic beverages including beer, wine, brandy, andwhiskey.